Distributed game coordination control method based on reinforcement learning for multi-microgrid

被引:0
|
作者
Liu, Wei [1 ]
Zhang, Sicong [1 ]
Zhang, Xiao-Ping [2 ]
Qian, Zhihao [1 ]
Hu, Tianhuan [1 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China
[2] Univ Birmingham, Dept Elect Elect & Syst Engn, Birmingham B15 2TT, Warwickshire, England
基金
中国国家自然科学基金;
关键词
Multi-microgrid (MMG); Potential game (PG); Q-learning; Distributed coordinated control;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The traditional centralized control method hardly meets the coordination control demand for multi-microgrid (MMG), due to the conflict between the individual interests of a single microgrid (MG) and the global interests of MMG. In this study, a distributed coordination control method that integrates potential game (PG) and reinforcement learning (RL) is proposed to achieve balance of interests of an MMG. The proposed method fully exploits the distributed characteristic of the PG by considering each MG as an agent. It also establishes a PG-based distributed coordination control structure to maximize and balance the economy of single MG and overall MMG. Then, it combines the PG with the RL algorithm by the parameter transfer to obtain the optimal Nash equilibrium (NE) solution and improve the optimization performance based on Q-learning algorithm. Eventually, a simulation model is performed in MATLAB to demonstrate the effectiveness and superiority of the proposed control method.
引用
收藏
页码:912 / 921
页数:10
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